You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.
You must test and deploy the trained model as a web service. The deployed model must meet the following requirements:
An authenticated connection must not be required for testing.
The deployed model must perform with low latency during inferencing.
The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.
You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.
Which compute resources should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Your team is building a data engineering and data science development environment.
The environment must support the following requirements:
* support Python and Scala
* compose data storage, movement, and processing services into automated data pipelines
* the same tool should be used for the orchestration of both data engineering and data science
* support workload isolation and interactive workloads
* enable scaling across a cluster of machines
You need to create the environment.
What should you do?

You build a data pipeline in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.
You need to run a Python script as a pipeline step.
Which two classes could you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

You need to build a feature extraction strategy for the local models.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You are using an Azure Machine Learning workspace. You set up an environment for model testing and an environment for production.
The compute target for testing must minimize cost and deployment efforts. The compute target for production must provide fast response time, autoscaling of the deployed service, and support real-time inferencing.
You need to configure compute targets for model testing and production.
Which compute targets should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.